AI/Machine Learning Engineer

RESPONSIBILITIES

& TASKS: AI Solution Development Design, develop, test, and deploy AI/ML models supporting business and operational outcomes. Build and maintain AI services, APIs, and microservices for enterprise consumption. Develop Generative AI and Agentic AI solutions using approved enterprise platforms and frameworks. Implement Retrieval Augmented Generation (RAG) architectures and knowledge retrieval solutions.

Develop prompt engineering, evaluation, and optimization approaches for AI systems. AI Platform Engineering and MLOps Develop and maintain AI model deployment pipelines and model lifecycle processes. Build automated testing, deployment, monitoring, model versioning, and release management capabilities. Support production deployment of models and AI services aligned to enterprise architecture standards.

Data Engineering and Integration Design and develop data pipelines required for AI model training and inference. Integrate AI solutions with enterprise applications, cloud platforms, APIs, databases, and data warehouses. Support data preparation, feature engineering, and operationalization of AI models. AI Operations, Monitoring, and Continuous Improvement Monitor model performance, accuracy, reliability, cost, and operational stability.

Implement observability and monitoring frameworks for AI workloads. Investigate production issues, perform root-cause analysis, and implement corrective actions. Tune and optimize models and AI services for performance, quality, and usability. Security, Governance, and Responsible AI Ensure AI solutions align with enterprise AI governance, information security, privacy, and data protection requirements.

Apply responsible AI principles including fairness, transparency, explain ability, reliability, and human oversight. Support AI risk assessments, security reviews, and compliance documentation. Collaboration and Enablement Work closely with business stakeholders, Data Scientists, solution architects, application teams, and platform engineers.

Participate in AI use case discovery, design workshops, technical reviews, and implementation planning. Support knowledge sharing and AI capability development across regional subsidaries. SKILLS & QUALIFICATIONS: Minimum 8 years of experience in software engineering, AI engineering, machine learning engineering, or related disciplines.

Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Information Technology, or a related discipline. Experience developing production-grade AI/ML solutions, working with cloud-native platforms, and implementing machine learning models in enterprise environments. Technical Skills Python Machine learning frameworks such as Py.

Torch, Tensor. Flow, and Scikit-Learn LLM and agent frameworks such as Lang. Graph, Lang. Chain, Semantic Kernel, or Auto. Gen API development and microservices Vector databases and RAG architectures MLOps platforms, CI/CD, and model lifecycle management SQL and data engineering Cloud services across AWS, Azure, or GCP Container technologies such as Docker and Kubernetes Git and DevOps practices